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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q40-Q45):

NEW QUESTION # 40
Which is NOT a built-in memory type in LangChain?

A. Conversation Buffer Memory
B. Conversation Token Buffer Memory
C. Conversation ImgeMemory
D. Conversation Summary Memory

Answer: C

NEW QUESTION # 41
Which statement is NOT true about StreamlitChatMessageHistory?

A. StreamlitChatMessageHistory can be used in any type of LLM application.
B. A given StreamlitChatMessageHistory will not be shared across user sessions.
C. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key.
D. A given StreamlitChatMessageHistory will NOT be persisted.

Answer: A

Explanation:
StreamlitChatMessageHistory is a chat message storage tool in Streamlit, used to manage message history within LLM-powered applications.
Key Features of StreamlitChatMessageHistory:
Stores chat messages within Streamlit's session state.
Not persistent across sessions; resets when the session is closed.
Specific to Streamlit applications, not designed for all LLM applications.
Why Option (D) is Incorrect:
StreamlitChatMessageHistory is designed for Streamlit-based apps.
It is not suitable for all LLM applications, particularly those requiring persistent storage.
Why Other Options Are Correct:
(A) True: Each session has its own instance of StreamlitChatMessageHistory.
(B) True: It is not persisted across sessions.
(C) True: It stores messages in the Streamlit session state.
🔹 Oracle Generative AI Reference:
While Oracle AI supports various LLM applications, StreamlitChatMessageHistory is limited to Streamlit-based chat interfaces.

NEW QUESTION # 42
Given the following code:
Prompt Template
(input_variable[''rhuman_input",'city''], template-template)
Which statement is true about Promt Template in relation to input_variables?

A. PromptTemplate supports Any number of variable*, including the possibility of having none.
B. PromptTemplate requires a minimum of two variables to function property.
C. PromptTemplate can support only a single variable M a time.
D. PromptTemplate is unable to use any variables.

Answer: A

Explanation:
The PromptTemplate in relation to input_variables is designed to be flexible and can support any number of variables, including the possibility of having none. This means that users can define a template with multiple variables or none at all, depending on their specific needs. The PromptTemplate facilitates dynamic prompt creation by inserting variable values into predefined template slots.
Reference
LangChain documentation on PromptTemplate
Examples and tutorials on using PromptTemplate in generative AI applications

NEW QUESTION # 43
What does a dedicated RDMA cluster network do during model fine-tuning and inference?

A. It limits the number of fine-tuned model deployable on the same GPU cluster.
B. It increases G PU memory requirements for model deployment.
C. It leads to higher latency in model inference.
D. It enables the deployment of multiple fine-tuned models.

Answer: D

Explanation:
A dedicated RDMA (Remote Direct Memory Access) cluster network is crucial during model fine-tuning and inference because it facilitates high-speed, low-latency communication between GPUs. This capability is essential for scaling up the deployment of multiple fine-tuned models across a GPU cluster.
RDMA allows data to be transferred directly between the memory of different computers without involving the CPU, leading to significantly reduced latency and higher throughput. This efficiency is particularly important in the context of fine-tuning and deploying large language models, where the speed and efficiency of data transfer can impact overall performance and scalability.
By enabling fast and efficient communication, a dedicated RDMA cluster network supports the deployment of multiple fine-tuned models on the same GPU cluster, enhancing both flexibility and scalability in handling various AI workloads.
Reference
Oracle Cloud Infrastructure (OCI) documentation on RDMA cluster networks Technical resources on the benefits of RDMA in high-performance computing environments

NEW QUESTION # 44
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

A. Determines the maximum number of tokens the model can generate per response
B. Specifies a string that tells the model to stop generating more content
C. Assigns a penalty to tokens that have already appeared in the preceding text
D. Controls the randomness of the model's output, affecting its creativity

Answer: D

Explanation:
The "temperature" parameter in generative AI models controls the randomness of the model's output. It affects the creativity and diversity of the generated text:
Low temperature: Leads to more deterministic and focused outputs, where the model tends to choose the most probable tokens, resulting in less randomness and creativity.
High temperature: Increases randomness by making the probability distribution over the next tokens flatter. This allows for more diverse and creative outputs, as the model is more likely to choose less probable tokens.
Adjusting the temperature parameter enables fine-tuning the balance between creativity and coherence in the model's responses.
Reference
Research articles on the role of temperature in generative models
Technical guides for tuning generative AI models in OCI

NEW QUESTION # 45
......

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